Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

ObjectiveIn nature, objects cast shadows due to illumination, forming the basis for stereoscopic perception. Birds need to adapt to changes in lighting (meaning they can recognize stereoscopic shapes even when shadows look different) to accurately perceive different three-dimensional forms. However, how neurons in the key visual brain area in birds handle these lighting changes remains largely unreported. In this study, pigeons (Columba livia) were used as subjects to investigate how neurons in pigeon’s ventrolateral mesopallium (MVL) represent stereoscopic shapes consistently, regardless of changes in lighting. MethodsVisual cognitive training combined with neuronal recording was employed. Pigeons were first trained to discriminate different stereoscopic shapes (concave/convex). We then tested whether and how light luminance angle and surface appearance of the stereoscopic shapes affect their recognition accuracy, and further verify whether the results rely on specify luminance color. Simultaneously, neuronal firing activity of neurons was recorded with multiple electrode array implanted from the MVL during the presentation of difference shapes. The response was finally analyzed how selectively they responded to different stereoscopic shapes and whether their selectivity was affected by the changes of luminance condition (like lighting angle) or surface look. Support vector machine (SVM) models were trained on neuronal population responses recorded under one condition (light luminance angle of 45°) and used to decode responses under other conditions (light luminance angle of 135°, 225°, 315°) to verify the invariance of responses to different luminance conditions. ResultsBehavioral results from 6 pigeons consistently showed that the pigeons could reliably identify the core 3D shape (over 80% accuracy), and this ability wasn’t affected by changes in light angle or surface appearance. Statistical analysis of 88 recorded neurons from 6 pigeons revealed that 83% (73/88) showed strong selectivity for specific 3D shapes (selectivity index>0.3), and responses to convex shapes were consistently stronger than to concave shapes. These shape-selective responses remained stable across changes in light angle and surface appearance. Neural patterns were consistent under both blue and orange lighting. The decoding accuracy achieves above 70%, suggesting stable responses under different conditions (e.g., different lighting angles or surface appearance). ConclusionNeurons in the pigeon MVL maintain a consistent neural encoding pattern for different stereoscopic shapes, unaffected by illumination or surface appearance. This ensures stable object recognition by pigeons in changing visual environments. Our findings provide new physiological evidence for understanding how birds achieve stable perception (“invariant neural representations”) while coping with variations in the visual field.

Cases of human infection with H7 subtype avian influenza virus(AIV)combined with five NA subtypes(N2,N3,N4,N7,and N9)have been reported.This study was aimed at establishing a method for simultaneous detection and dif-ferential diagnosis of H7 and five NA subtypes of AIV.Seven pairs of specific primers were designed according to the conserved sequences of the HA gene of H7 subtype AIV,the NA gene of five NA AIV subtypes,and the M gene of all AIV subtypes.A high-throughput GeXP typing method was established for simultaneous detection of the H7 subtype and the five NA subtypes of AIV by using GeXP multiple gene expression and capillary electrophoresis analysis technology.The specificity and sensitivity of the method were determined,and clinical samples were tested.The specificity results indicated that this method was able to simultaneously detect seven target genes in a single tube;each pair of specific primers was able to detect the corresponding AIV subtype,and the universal detection primers were able to detect all subtypes of AIV,with no cross-reaction with other common avian disease pathogens.Sensitivity results demonstrated that this method was able to simultaneously detect seven target genes with a threshold detection limit was 100 copies/μL.The detection results for 150 clinical samples were consistent with those of viral isolation and identification.The high-throughput GeXP method for simultaneous differential diagnosis of the H7 subtype and five subtypes of AIV established in this study has advantages of high specificity,high sensitivity,rapidity,and simplicity,thus providing a new detection method for the effective prevention and control of AIV.

This work was aimed at predicting and verifying B-cell epitopes of SARS-CoV-2 E protein through bioinformatics methods,and clarifying the dominant B cell epitopes with mouse polyclonal antibody serum prepared through SARS-CoV-2 re-combinant E protein immunization and human positive serum vaccinated with inactivated SARS-CoV-2 vaccine.The structural and B-cell linear epitopes of SARS-CoV-2 E protein were predicted with SOPMA,Expasy,SWISS-MODEL,IEDB database,and Bepid-2.0 software.Candidate epitopes were expressed as GST-tagged recombinant protein fragments in an E.coli sys-tem,and their immunoreactivity with mouse and human poly-clonal positive serum against SARS-CoV-2 E protein was de-tected by western blotting and indirect ELISA,respectively.The epitope prediction results showed that E protein contained linear B cell epitopes Ser6-Val14 and Tyr57-Pro71,and the conformational epitopes of Glu8-Val12,Leu39-Tyr59,and Ser60-Leu65.The GST tagged recombinant E protein fragments of E1 and E3,containing Ser6-Val14 and Tyr57-Pro71 epitopes,respectively,as well as E2 without an epitope sequence as a control,were expressed in an E.coli expression system and successfully purified with an Ni-NTA column.Western blotting and indirect ELISA analysis indicated that all mouse and human SARS-CoV-2 positive sera positively reacted with only E1 and E3 proteins,but negatively reacted with E2 protein,thus indicating that the corresponding epitope prediction with Ser6-Val14 and Tyr57-Pro71 was correct.This study successfully predicted and preliminarily identified two linear B cell epitopes of SARS-CoV-2 E protein,thus providing a reference for the preparation of new coronavirus vaccines and the analysis of immune response characteristics.

Objective:Analyze the medical reimbursement rate and influencing factors under the DIP payment method to refine the DIP payment policy,promote the optimization of internal operations in medical institutions,and ensure reasonable compensation.Methods:Based on the 2022 DIP fund settlement data from 196 medical institutions in City A,the study used multiple linear regression to analyze the factors affecting medical reimbursement rate and conducted a heterogeneity analysis for medical institutions of different levels.Results:The medical reimbursement rate for medical institutions in City A in 2022 was 103.32%.Medical institutions with lower CMI standardized inpatient costs,lower rates of deviation cases,tertiary care institutions,lower proportion of level-four surgeries,and lower ratios of resident to employee medical insurance cases have higher medical reimbursement rate(P<0.05).Heterogeneity analysis reveals that therates of deviation cases,the proportion of primary care diseases,the ratio of resident to employee medical insurance cases,and the low-standard admission rate have different impacts on medical institutions of different levels.Conclusion:Medical insurance departments should improve policies for primary care diseases,dynamically adjust disease catalogs and payment standards,optimize funding levels and institutional coefficients,and increase penalties for violations to ensure effective use of funds.Medical institutions need to strengthen their understanding of policies,focus on refined internal management,promote standardized and rational diagnosis and treatment through performance assessment transformation,and leverage their own advantages in medical services to reasonably increase the medical reimbursement rate.

Objective:To analyze the efficiency of human resource allocation and its spatial distribution characteristics of district-level Center for Disease Control and Prevention(CDC)in city N of Jiangsu Province in 2020,in order to provide a strong decision-making reference for optimizing and strengthening the CDC talent team.Methods:The efficiency of human resources of district-level CDC of City N in2020 was measured using the Super-Efficiency SBM model,and the spatial association pattern was analyzed using the natural break point classification method and Moran's index,with the visualization presented through LISA maps.Results:The overall level of human resource efficiency in district-level CDC of City N is relatively high.However,spatially,there are significant differences among the regions,showing a trend of high efficiency in the central areas and low efficiency at the ends.Moran's index and LISA maps indicate a negative spatial correlation in efficiency,with a low-high(L-H)cluster centered on Area L and a high-low(H-L)cluster centered on Area J.The high-high(H-H)cluster pattern has not yet formed,exhibiting a characteristic of interspersed high and low efficiency.Conclusions:There are regional differences in the human resource efficiency of the Disease Control Center in City N,and the spatial cluster pattern needs to be optimized.It is recommended to focus on efficiency improvement in Areas P and L,formulate appropriate policies,and promote coordinated regional development.